Erik Olsson

Fault Diagnosis of Industrial Robots Using Acoustic Signals and Case-Based Reasoning

In industrial manufacturing rigorous testing is used to ensure that the delivered products meet their specifications. Mechanical maladjustment or faults often show their presence through abnormal acoustic signals. This is the same case in robot assembly – the application domain addressed in this paper. Manual diagnosis based on sound requires extensive experience, and usually such experience is acquired at the cost of reduced production efficiency or degraded product quality due to mistakes in judgments. The acquired experience is also difficult to preserve and transfer and it often gets lost if the corresponding personnel leave the task of testing. We propose herein a Case-Based Reasoning approach to collect, preserve and reuse the available experience for robot diagnosis. This solution enables fast experience transfer and more reliable and informed testing. Sounds from normal and faulty robots are recorded and stored in a case library together with their diagnosis results. Given an unclassified sound signal, the relevant cases are retrieved from the case library as reference for deciding the fault class of the new case. Adding new classified sound profiles to the case library improves the system’s performance. So far the developed system has been applied to the testing environment for industrial robots. The preliminary results demonstrate that our system is valuable in this application scenario in that it can preserve and transfer the related experience among technicians and shortens the overall testing time.

Agent-Based Monitoring using Case-Based Reasoning for Experience Reuse and Improved Quality

Purpose – The purpose with this paper is to propose an agent-based condition monitoringsystem for use in industrial applications. An intelligent maintenance agent is described that isable to autono ...

Case-Based Reasoning for Medical and Industrial Decision Support Systems

The amount of medical and industrial experience and knowledge is rapidly growing and it is increasingly difficult to be up to date leading to less informed decisions, mistakes and serious accidents. The demand for decision support system (DSS) is especially important in domains where experience and knowledge grow rapidly. However, traditional approaches to DSS are not always easy to adapt to a flow of new experience and knowledge and may also show a limitation in areas with a weak domain theory. This chapter explores the functionalities of Case-Based Reasoning (CBR) to facilitate experience reuse both in clinical and in industrial decision making tasks. Examples from the field of stress medicine and condition monitoring in industrial robots are presented here to demonstrate that the same approach assists both clinical applications as well as decision support for engineers. In both domains, DSS deals with sensor signal data and integrate other artificial intelligence techniques into the CBR system to enhance performance in a number of different aspects. Textual information retrieval, Rule-Based Reasoning (RBR), and fuzzy logic are combined together with CBR to offer decision support to clinicians for a more reliable and efficient management of stress. Agent technology and wavelet transformations are applied with CBR to diagnose audible faults on industrial robots and to package such a system. The performance of the CBR systems have been validated and have shown to be useful in solving such problems in both of these domains.